The first plot shows all the environment indicators from both the current studies and the original framework in the y-axis. Purple indicates that the indicator is only being used in the current studies, orange that it is only included in the Wiltshire framework, and green that the indicator is used in both the framework and current studies.
The x-axis shows the number of secondary data metrics that have been collected to represent those indicators. You can see that there are some indicators for which there exist many data, but many indicators for which I have found little to represent them.
NASS figures are used to cover on-farm water use, energy efficiency, and acres in conservation practices. I used the National Aquatic Resource Surveys aggregated at the state level to measure water quality. Land use diversity is pretty well represented by Multi-Resolution Land Characteristics LULC layers, which I also aggregated at the county level. Greenhouse gas emissions come from EPA figures by state, broken down by economic sector. Finally, the USFS TreeMap dataset accounts for aboveground biomass and would do reasonably well in tree vigor. There is more to pull out here than I have so far.
Otherwise, if anyone has ideas for secondary datasets to cover the rest of the indicators, please do let me know.
Code
pacman::p_load( dplyr, ggplot2, stringr, plotly, RColorBrewer)## Load data for tree and metricsenv_tree <-readRDS('data/trees/env_tree.rds')meta <-readRDS('data/sm_data.rds')[['metadata']] %>%filter(dimension =='environment')# Format to match Wiltshire frameworkmeta <- meta %>%mutate(indicator =str_to_sentence(indicator),indicator =case_when(str_detect(indicator, '^Above') ~'Aboveground biomass',str_detect(indicator, '^Water') ~'Water use / irrigation efficiency',TRUE~ indicator ) ) # Counts of secondary data metricscounts <- meta %>%group_by(indicator) %>% dplyr::summarize(count =n())# Join to Wiltshire frameworkcolors <- RColorBrewer::brewer.pal(n =3, name ='Dark2')dat <-full_join(env_tree, counts, by =join_by(Indicator == indicator)) %>%mutate(count =ifelse(is.na(count), 0, count),label_color =case_when( Use =='both'~ colors[1], Use =='wiltshire_only'~ colors[2], Use =='current_only'~ colors[3] ) )# Plotdat %>%ggplot(aes(x = Indicator, y = count)) +geom_col(color ='black',fill ='grey' ) +geom_point(data = dat,aes(x =1, y =1, color = Use),inherit.aes =FALSE ) +scale_color_manual(name ="Indicator Use:",values =c("both"= colors[1],"current_only"= colors[3],"wiltshire_only"= colors[2] ),labels =c('Both','Current Only','Framework Only' ) ) +theme_classic() +theme(axis.text.y =element_text(color = dat$label_color),legend.position ="bottom",plot.margin =margin(t =10, r =75, b =10, l =10) ) +guides(color =guide_legend(override.aes =list(size =3)) ) +coord_flip() +labs(y ='Secondary Data Count')
Bar Plot of Indicators
2 Maps
Taking a quick tour through some of the spatial data here. I’m not including full rasters of LULC or TreeMap layers to conserve space, but I have included some derived metrics by county. With the exception of hotspot and species atlas data, these will also be up on the Shiny app along with all the other metrics.
Land Use Diversity
This is derived from the USGS MRLC 30m LULC layer for 2023. LULC types are aggregated by category (water, developed, barren, forest, shrubland, herbaceous, cultivated, wetlands) and Shannon diversity is calculated for each county.
Again, this biodiversity hotspot map was being put together around the same time as the Y2k crisis. Even if this were more recent and throughout New England, incoporating this kind of data into the framework seems a bit fraught.
The TreeMap 2016 dataset is quite comprehensive national survey of forest health and diversity. Updates are infrequent, but this is the best layer I’ve found to address biomass. The raster is at 30m. Shown below is the mean live above-ground biomass aggregated by county so that it plays well with other metrics. Note that it is measured in tons per acre of forest, non-forest cells were removed from analysis. So, it is not showing density of forest, just biomass in existing forest. This is why the more urban counties still show a reasonable density of live biomass. There is lots more that can be pulled out of this dataset, like dead/down carbon, tree stocking, live canopy cover, height, volume, tree per acre, etc. More info can be found here.
pacman::p_load( dplyr, reactable, stringr, htmltools)# Load metrics and metadatametadata_all <-readRDS('data/sm_data.rds')[['metadata']]metrics <-readRDS('data/sm_data.rds')[['metrics']]fips_key <-readRDS('data/sm_data.rds')[['fips_key']]# Value formatting function based on unitssource('dev/format_values.R')# Filter to economics metrics, join with metadata and county fips codesenv_metrics <- metrics %>%left_join(metadata_all, by =join_by('variable_name')) %>%filter(dimension =='environment') %>%left_join(fips_key, by =join_by('fips')) %>%mutate(county_name =ifelse(is.na(county_name), state_name, county_name)) %>%format_values() %>%select( metric,'Variable Name'= variable_name, definition,year = year.x,Area = county_name, units, value ) %>%setNames(c(str_to_title(names(.)))) %>%filter(!is.na(Value))## Reactable tablehtmltools::browsable(tagList( tags$div(style ="display: flex; gap: 16px; margin-bottom: 20px; justify-content: center;", tags$button(class ="btn btn-primary",style ="display: flex; align-items: center; gap: 8px; padding: 8px 12px;",tagList(fontawesome::fa("download"), "Download as CSV"),onclick ="Reactable.downloadDataCSV('metrics_table', 'sustainability_metrics.csv')" ) ),reactable( env_metrics,sortable =TRUE,resizable =TRUE,filterable =TRUE,searchable =TRUE,pagination =TRUE,bordered =TRUE,wrap =TRUE,rownames =FALSE,onClick ='select',striped =TRUE,pageSizeOptions =c(5, 10, 25, 50, 100),defaultPageSize =5,showPageSizeOptions =TRUE,highlight =TRUE,style =list(fontSize ="14px"),compact =TRUE,columns =list(Metric =colDef(minWidth =125,sticky ='left' ),'Variable Name'=colDef(minWidth =125 ),Definition =colDef(minWidth =250 ),Units =colDef(minWidth =100),'Year'=colDef(minWidth =100) ),defaultColDef =colDef(minWidth =100),elementId ="metrics_table" ) ))
5 Distribution Plots
By County
Note that while most of the available secondary data is at the county level, the environment dimension includes a fair amount at the state level as well. This includes greenhouse gas emissions and water quality surveys. For now, I’ll just show these separately, but some creative aggregation will have to happen eventually.
Code
pacman::p_load( dplyr, purrr, ggplot2, rlang, ggpubr, tidyr)source('dev/data_pipeline_functions.R')source('dev/filter_fips.R')metrics <-readRDS('data/sm_data.rds')[['metrics']]metadata <-readRDS('data/sm_data.rds')[['metadata']]# Use metadata to get help filter by dimensionenv_meta <- metadata %>%filter(dimension =='environment')# Filter to economics dimensionenv_metrics <- metrics %>%filter(variable_name %in% env_meta$variable_name)# env_metrics$variable_name %>% unique# get_str(env_metrics)# Filter to latest year and new (post-2024) counties# And pivot wider so it is easier to get correlationsenv_county <- env_metrics %>%filter_fips(scope ='counties') %>%get_latest_year() %>%select(fips, variable_name, value) %>%mutate(variable_name =str_split_i(variable_name, '_', 1)) %>%pivot_wider(names_from ='variable_name',values_from ='value' ) %>%unnest(!fips) %>%mutate(across(c(2:last_col()), as.numeric))# get_str(env_county)## Plotplots <-map(names(env_county)[-1], \(var){if (is.character(env_county[[var]])) { env_county %>%ggplot(aes(x =!!sym(var))) +geom_bar(fill ='lightblue',color ='royalblue',alpha =0.5 ) +theme_classic() +theme(plot.margin =unit(c(rep(0.5, 4)), 'cm')) } elseif (is.numeric(env_county[[var]])) { env_county %>%ggplot(aes(x =!!sym(var))) +geom_density(fill ='lightblue',color ='royalblue',alpha =0.5 ) +theme_classic() +theme(plot.margin =unit(c(rep(0.5, 4)), 'cm')) } else {return(NULL) }}) # Arrange them in 4 columnsggarrange(plotlist = plots,ncol =4,nrow =7)
Distributions of economic metrics at the county level.
By State
Code
pacman::p_load( dplyr, purrr, ggplot2, rlang, ggpubr, tidyr)state_codes <-readRDS('data/sm_data.rds')[['fips_key']] %>%select(fips, state_code)env_state <- env_metrics %>%filter_fips(scope ='state') %>%get_latest_year() %>%select(fips, variable_name, value) %>%mutate(variable_name =str_split_i(variable_name, '_', 1)) %>%pivot_wider(names_from ='variable_name',values_from ='value' ) %>%unnest(!fips) %>%mutate(across(c(2:last_col()), as.numeric)) %>%left_join(state_codes, by ='fips')# get_str(env_state)# Variables to map. Take out some that didn't come through well.vars <-names(env_state)[-1] %>%str_subset('lakesAcidCond|lakesCylsperEpaCond|lakesMicxEpaCond|state_code|waterIrrSrcOffFarmExp|waterIrrReclaimedAcreFt|waterIrrReclaimedOpenAcres',negate =TRUE )## Plotplots <-map(vars, \(var){ env_state %>%ggplot(aes(y =!!sym(var), x = state_code, color = state_code)) +geom_point(alpha =0.5,size =3 ) +theme_classic() +theme(plot.margin =unit(c(rep(0.5, 4)), 'cm'),legend.position ='none' ) +labs(x ='State' )}) # Arrange them in 4 columnsggarrange(plotlist = plots,ncol =4,nrow =17)
Distributions of environmental variables at state level
6 Bivariate Plots
Using a selection of variables at the county level. The variable names are a bit hard to fit in here, but from left to right across the top they are LULC diversity, mean live above-ground forest biomass, conservation income per farm, conservatino easement acres per farm, conservation tillage: no-till acres per farm, conservation tillage: excluding no-till acres per farm, and cover cropping: excluding CRP acres per farm.
Code
pacman::p_load( GGally)# Neat function for mapping colors to ggpairs plots# https://stackoverflow.com/questions/45873483/ggpairs-plot-with-heatmap-of-correlation-valuesmap_colors <-function(data, mapping,method ="p",use ="pairwise", ...) {# grab data x <-eval_data_col(data, mapping$x) y <-eval_data_col(data, mapping$y)# calculate correlation corr <-cor(x, y, method = method, use = use) colFn <-colorRampPalette(c("blue", "white", "red"), interpolate ='spline') fill <-colFn(100)[findInterval(corr, seq(-1, 1, length =100))]# correlation plotggally_cor(data = data, mapping = mapping, color ='black', ...) +theme_void() +theme(panel.background =element_rect(fill = fill))}lower_function <-function(data, mapping, ...) {ggplot(data = data, mapping = mapping) +geom_point(alpha =0.5) +geom_smooth(color ="blue", fill ="grey", ...) +theme_bw()}# Rename variables to be shorterenv_county %>%select(LULC = lulcDiversity,Biomass = meanAboveGrndForBiomass, consIncomePF, consEasementAcresPF, consTillNoTillAcresPF, consTillExclNoTillAcresPF, coverCropExclCrpAcresPF ) %>%ggpairs(upper =list(continuous = map_colors),lower =list(continuous = lower_function),axisLabels ='show' ) +theme(strip.text =element_text(size =5),axis.text =element_text(size =5),legend.text =element_text(size =5) )
7 Correlations
Only showing correlations by county because we don’t have enough observations to run it by state.
Code
pacman::p_load( dplyr, tidyr, tibble, stringr, purrr, tidyr, ggplot2, plotly, reshape, Hmisc, viridisLite)# get_str(env_county)cor <- env_county %>%select(-fips) %>%as.matrix() %>%rcorr()# Melt correlation values and rename columnscor_r <-melt(cor$r) %>%setNames(c('var_1', 'var_2', 'value'))# Save p valuescor_p <-melt(cor$P)p.value <- cor_p$value# Make heatmap with custom text aesthetic for tooltipplot <- cor_r %>%ggplot(aes(var_1, var_2, fill = value, text =paste0('Var 1: ', var_1, '\n','Var 2: ', var_2, '\n','Correlation: ', format(round(value, 3), nsmall =3), '\n','P-Value: ', format(round(p.value, 3), nsmall =3) ))) +geom_tile() +scale_fill_viridis_c() +theme(axis.text.x =element_text(hjust =1, angle =45)) +labs(x =NULL,y =NULL,fill ='Correlation' )# Convert to interactive plotly figure with text tooltipggplotly( plot, tooltip ='text',width =1000,height =800)
Interactive correlation plot of metrics by county
8 PCA
Again, this is only at the county level. First, imputing missing data.
Code
pacman::p_load( missForest)# Wrangle dataset. Need all numeric vars or factor vars. And can't be tibble# Also removing character vars - can't use these in PCAdat <- env_county %>%select(where(is.numeric)) %>%as.data.frame()# get_str(dat)# Impute missing variablesset.seed(42)mf_out <- dat %>%missForest(ntree =200,mtry =10,verbose =FALSE,variablewise =FALSE )# Save imputed datasetimp <- mf_out$ximp# Print OOBmf_out$OOBerror
The scree plot makes a pretty good case for 3 components here as well, as it has a nice elbow after the third.
It looks like the first component is made up of most of the conservation agriculture practices from the NASS datasets, namely acres of conservation tillage, cover cropping, and draining. Fertilizer expenses loads surprisingly strongly here too. The second component seems to have the most to do with county size or population; anything measured by the number of operations does well here, as does mean above-ground forest biomass. The last component is a grab-bag - conservation easement acres load the strongest onto it, but I don’t see a coherent pattern among metrics here.
9 Analysis
Imputation
Code
pacman::p_load( missForest)# Wrangle dataset. Need all numeric vars or factor vars. And can't be tibble# Also removing character vars - can't use these in PCA# Using old Connecticut counties - some lulc data is missing for them thoughdat <- env_county %>%filter_fips('old') %>%select(fips, where(is.numeric)) %>%column_to_rownames('fips') %>%as.data.frame()# get_str(dat)# skimr::skim(dat)# Remove variables with most missing data - too much to impute.# Also remove the proportional LULC values - keeping diversity thoughdat <- dat %>%select(-matches('consIncome'), -matches('^lulcProp'))# Impute missing variablesset.seed(42)mf_out <- dat %>%missForest(ntree =200,mtry =10,verbose =FALSE,variablewise =FALSE )# Save imputed datasetimp <- mf_out$ximp# Print OOBmf_out$OOBerror
NRMSE
0.00142119
Normalization
Using min-max normalization to put all indicators on an arbitrary 0-1 scale
Code
# get_str(imp)# Function to normalize datanormalize <-function(x) { (x -min(x, na.rm =TRUE)) / (max(x, na.rm =TRUE) -min(x, na.rm =TRUE))}# Normalize all variablesdat <-map_dfc(imp, normalize)
Now that we have normalized variables, we have to make normative decisions about what constitutes a good or bad value of those variables. This will eventually be a collaborative process where we will seek input from teams to come to some kind of consensus. But until then, I’m going to make some heroic assumptions that LULC diversity is good, above ground forest biomass is good, conservation practices and easements are good, and fertilizer expenses are bad. Open to thoughts here as always.
With that, we can recode our normalized variables accordingly.
Code
# Flip 'bad' variables by subtracting them from 1normed <- dat %>%mutate(across(c(matches('^fert')), ~1- .x))# get_str(normed)
Component Extraction
Determine the number of components to extract using a few tools: very simple structure (VSS), Velicer’s minimum average partial (MAP) test, parallel analysis, and a scree plot.
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
VSS suggests 1 or 2, MAP suggests 8, parallel analysis shows 3. I’m going with 3 here, which will be explained further below.
Principal Components Analysis
Using an oblique varimax rotation that allows for correlations between principal components. First we just observe the scree plot to see how the components play out.
This scree plot shows the eigenvalues (variance explained) of each principal component (y-axis) against each component (x-axis). The first few components explain lots of variance, but there is a decent elbow around the fourth component. Just looking at this plot, I would be tempted to take four, but the results below suggest three is more appropriate for our application.
Recommendations for creating composite indices are to extract components that each have eigenvalues > 1, explained variance > 0.10, and such that the proportion of explained variance for the total set is > 0.60 (Nicoletti 2000; OECD 2008).
Our total cumulative variance is explained is 0.74, and our component that explains the least variance is RC4 with 0.11. Note that extracting four or more components here gives us a component with less than 0.10, so this is why we are sticking to three. The first component (RC1) explains 38% of the variance in the data. The second component is respectable at 0.26, while the third is barely above the threshold at 0.11.
Looking at the metrics, we can see that the first component loads mostly onto the conservation practices, no-till acres, cover cropping, drainage, and total fertilizer expenses. The second component leads onto mean aboveground biomass (although there is crossloading with the first component), operations wtih silvapasture, operations with easements, rotational grazing operations, and operations with fertilizer expenses. This seems to be catching more of the population-related metrics. The last component only loads onto a few metrics: easement acres, easement acres per farm, and silvpasture operations (which has some heavy crossloading).
Nicoletti, Giuseppe. 2000. “Summary Indicators of Product Market Regulation with an Extension to Employment Protection Legislation.” {{OECD Economics Department Working Papers}} 226. Vol. 226. OECD Economics Department Working Papers. https://doi.org/10.1787/215182844604.
OECD. 2008. Handbook on Constructing Composite Indicators: Methodology and User Guide. Paris: Organisation for Economic Co-operation and Development.
